LINEA

Getting Started

Dark Mode

LINEA

LINEA is an R library aimed at simplifying and accelerating the development of linear models to understand the relationship between two or more variables.

Linear models are commonly used in a variety of contexts including natural and social sciences, and various business applications (e.g. Marketing, Finance).

This page covers a basic implementation of the linea library to analyse a time-series. We’ll cover:

Installation: Installing and checking the library.
Data Ingestion: Importing data for this example.
First Model: Running a basic model.
Visualisation: Visualizing a model’s output.
Additional Features: Other key features of the library.

We will run a simple model on some fictitious data sourced from Google trends. The aim of this exercise will be understand what variables seem to have an impact on the ecommerce variable.

Prerequisites

To use this library an understanding of the following is assumed:

Installation

The library can be installed from github as follows:

devtools::install_github('paladinic/linea')

Once installed you can check the installation.

print(packageVersion("linea"))

## [1] '0.0.1'

The linea library works well with pipes. Used with dplyr and plotly, it perform data analysis and visualisation with elegant code.

library(linea) # the library in question

library(dplyr) # for pipes (%>%) and data manipulation
library(plotly) # for interactive charts
library(DT) # for interactive tables

Data Ingestion

The function linea::read_xcsv() can be used to read csv or excel files.

# data_path = 'c:/Users/44751/Desktop/github/data/ecomm_data.csv'
data_path = 'https://raw.githubusercontent.com/paladinic/data/main/ecomm_data.csv'
data = read_xcsv(file = data_path)
data %>%
  datatable(rownames = NULL,
            options = list(scrollX = TRUE))

First Model

As shown above, the data contains several variables including the ecommerce variable, other numeric variables, and a date-type variable (i.e. date). With this data we can start building models to understand which variables have an impact on ecommerce. The linea::run_model() function can be used to run an OLS regression model. Some of the function’s arguments are:

dv: dependent variable name
ivs: independent variable name (character vector)
data: data with variables (data.frame)

The function will return an “lm” object like the one from the stats::lm() function which can be inspected with the base::summary() function.

model = run_model(data = data,
                  dv = 'ecommerce',
                  ivs = c('covid','christmas'),
                  id_var = 'date')

summary(model)

## 
## Call:
## lm(formula = formula, data = trans_data[, c(dv, ivs_t)])
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -34222  -5723   -106   4361  64271 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 56339.61     690.61   81.58   <2e-16 ***
## covid         336.41      19.61   17.16   <2e-16 ***
## christmas     383.15      30.65   12.50   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 8947 on 258 degrees of freedom
## Multiple R-squared:  0.6339, Adjusted R-squared:  0.6311 
## F-statistic: 223.4 on 2 and 258 DF,  p-value: < 2.2e-16

Visualisation

Models can be inspected visually using the linea::decomping() function. Some of the function’s arguments are:

model: the model (lm) object
id_var: the name of the id variable (i.e. “date”; optional)
raw_data: the data containing modeled and id variable (optional)

decomposition = model %>% decomping()

print(names(decomposition))

## [1] "category_decomp" "variable_decomp" "fitted_values"

The decomposition object is a list of 3 data frames. These can be viewed directly using the functions linea::fit_chart() and linea::decomp_chart().

Decomposition

The first 2, variable_decomp and category_decomp, capture the role of individual variables in the model (categories can be set to group variables).

decomposition$variable_decomp %>%
  datatable(rownames = NULL,
            options = list(scrollX = T))

The linea::decomp_chart() function can be used to display a stacked bar chart of the decomposition.

model %>%
  decomp_chart()

Prediction, Actual, Error

The fitted_values dataframe instead contains the dependent variable (actual), the model prediction (model), and the error (residual).

decomposition$fitted_values %>%
  datatable(rownames = NULL,
            options = list(scrollX = T))

The linea::fit_chart() function can be used to display a line chart of the Prediction, Actual, and Error.

model %>%
  fit_chart()

Diagnostic Charts

The linea::acf_chart() and linea::resid_hist_chart() functions can be used to assess your model as per the assumptions of linear regression:

Linear relationship
Residual Normality
Autocorrelation
Heteroscedasticity
Multi-collinearity

Using the linea::acf_chart() function we can visualize the ACF, which helps us detect Autocorrelation.

model %>%
  acf_chart()

Using the linea::resid_hist_chart() function we can visualize the distribution on residuals, which helps us detect Residual Normality.

model %>%
  resid_hist_chart()

Response Curves

Using the linea::response_curves() function we can visualize the relationship between the independent variables and the dependent variable.

model %>%
  response_curves()

Additional Features

Seasonality

Using a date column, of data-type date, we can generate seasonality variables with linea::get_seasonality(). Several columns will be added to the original dataframe. These are dummy variables that capture some basic holidays as well as year, month, and week number. Also a trend variable is added which is a column that goes form 1 to n, where n is the number of rows.

data = data %>%
  get_seasonality(
    date_col_name = 'date',
    date_type = 'weekly ending')

data %>%
  datatable(rownames = NULL,
            options = list(scrollX = TRUE))

plot_ly(data) %>%
  add_bars(y = ~ week_26,
           x = ~ date,
           name = 'week_26',
           color = color_palette()[1]) %>%
  add_bars(y = ~ new_years_eve,
           x = ~ date,
           name = 'new_years_eve',
           color = color_palette()[2]) %>%
  add_bars(y = ~ year_2019,
           x = ~ date,
           name = 'year_2019',
           color = color_palette()[3]) %>%
  layout(yaxis = list(title = 'value'),
         title = 'Seasonality Variables',         
         plot_bgcolor  = "rgba(0, 0, 0, 0)",
         paper_bgcolor = "rgba(0, 0, 0, 0)")

These variables can be used in the model to capture the seasonal component of the dependent variable, among other things (e.g. trend).

model = run_model(data = data,
                  dv = 'ecommerce',
                  ivs = c('covid', 'christmas', 'trend','month_Dec'),
                  id_var = 'date')

summary(model)

## 
## Call:
## lm(formula = formula, data = trans_data[, c(dv, ivs_t)])
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -33092  -3553  -1086   3033  60996 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 47639.504   1072.631  44.414  < 2e-16 ***
## covid         180.013     23.508   7.657 3.89e-13 ***
## christmas     446.518     39.255  11.375  < 2e-16 ***
## trend          84.042      8.825   9.524  < 2e-16 ***
## month_Dec   -7638.941   2650.433  -2.882  0.00428 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7626 on 256 degrees of freedom
## Multiple R-squared:  0.7361, Adjusted R-squared:  0.732 
## F-statistic: 178.5 on 4 and 256 DF,  p-value: < 2.2e-16

Thanks to the new variables this model has a better r-squared compared to the previous. These variables’ impact can be clearly seen using the linea::decomping() and linea::decomp_chart() functions.

model %>%
  decomp_chart()

Transformations

linea provides a few default transformations. These are mathematical functions meant to capture non linear relationships in the data. These functions are embedded in the library but are also available for use.

Decay

The linea::decay() function applies a decay by adding to each data point a percentage of the previous. This transformation is meant to capture the impact, over time, of an event. This function only makes sense on time-bound models.

raw_variable = data$online_media
dates = data$date

plot_ly() %>%
  add_lines(y = raw_variable, x = dates, name = 'raw') %>%
  add_lines(y = decay(raw_variable, decay = 0.5),
            x = dates,
            name = 'transformed: decay 50%') %>%
  add_lines(y = decay(raw_variable, decay = 0.75),
            x = dates,
            name = 'transformed: decay 75%') %>%
  add_lines(y = decay(raw_variable, decay = 0.95),
            x = dates,
            name = 'transformed: decay 95%') %>%
  layout(title = 'decay',
         xaxis = list(showgrid = F),
         plot_bgcolor  = "rgba(0, 0, 0, 0)",
         paper_bgcolor = "rgba(0, 0, 0, 0)")

Diminish

The linea::diminish() function applies a negative exponential function by adding to each data point a percentage of the previous. This transformation is meant to capture relationships that exhibit diminishing returns.

raw_variable = data$ecommerce
dates = data$date

plot_ly() %>%
  add_lines(y = raw_variable, x = dates, name = 'raw') %>%
  add_lines(
    y = diminish(raw_variable, m = 0.3, abs = F),
    x = dates,
    name = 'transformed: diminish 30%',
    yaxis = "y2"
  ) %>%
  layout(title = 'diminish',
         yaxis2 = list(overlaying = "y",
                       showgrid = F,
                       side = "right"), 
         xaxis = list(showgrid = F),
         plot_bgcolor  = "rgba(0, 0, 0, 0)",
         paper_bgcolor = "rgba(0, 0, 0, 0)")

This transformation can also be visualized by placing the raw and transformed variable on the horizontal and vertical axis.

plot_ly() %>% 
  add_trace(
      x = raw_variable,
      y = diminish(raw_variable,.3,F)
    ) %>% 
  layout(title = 'raw vs. diminished', 
         xaxis = list(showgrid = F),
         plot_bgcolor  = "rgba(0, 0, 0, 0)",
         paper_bgcolor = "rgba(0, 0, 0, 0)")

Lag

The linea::lag() function applies a lag to the data. This transformation is meant to capture relationships that are lagged in time. This function only makes sense on time-bound models.

raw_variable = data$ecommerce
dates = data$date

plot_ly() %>% 
  add_lines(y = raw_variable, x = dates, name = 'raw') %>%
  add_lines(
    y = lag(raw_variable, n = 1),
    x = dates,
    name = 'transformed: lag 1',
  ) %>%
  add_lines(
    y = lag(raw_variable, n = 5),
    x = dates,
    name = 'transformed: lag 5',
  ) %>%
  add_lines(
    y = lag(raw_variable, n = 10),
    x = dates,
    name = 'transformed: lag 10',
  )  %>% 
  layout(plot_bgcolor  = "rgba(0, 0, 0, 0)",
         paper_bgcolor = "rgba(0, 0, 0, 0)",
         title = 'lag',
         xaxis = list(showgrid = F))

Moving Average

The linea::ma() function applies a moving average to the data. This transformation is meant to capture relationships that are smoothed over time. This function only makes sense on time-bound models.

raw_variable = data$black.friday
dates = data$date

plot_ly() %>% 
  add_lines(y = raw_variable, x = dates, name = 'raw') %>%
  add_lines(
    y = ma(raw_variable, width = 5),
    x = dates,
    name = 'transformed: ma 5',
  ) %>%
  add_lines(
    y = ma(raw_variable, width = 15),
    x = dates,
    name = 'transformed: ma 15',
  ) %>% 
  add_lines(
    y = ma(raw_variable, width = 25),
    x = dates,
    name = 'transformed: ma 25',
  ) %>% 
  add_lines(
    y = ma(raw_variable, width = 25,align = 'left'),
    x = dates,
    name = 'transformed: lag 25 left',
  ) %>% 
  layout(plot_bgcolor  = "rgba(0, 0, 0, 0)",
         paper_bgcolor = "rgba(0, 0, 0, 0)",
         xaxis = list(showgrid = F),
         title='ma')

Non-linear models

linea can capture non-linear relationships by applying transformations to the raw data, and then generating the regression for the transformed data. This can be accomplished using a model table which specifies each variable’s transformation parameters. The function linea::build_model_table() can be used to generate the blank model table.

model_table = build_model_table(ivs =  c('covid','christmas','trend'))

model_table %>%
  datatable(rownames = NULL,
            options = list(scrollX = T,
                           dom = "t"))

The model table can be written as a CSV or Excel and modified outside of R, or using dplyr as shown below. In this example the model run will apply the linea::dim_rets() function (with a parameter of 0.5, to the “covid” variable.

model_table = model_table %>%
  mutate(diminish = if_else(variable ==  'covid','10',diminish)) %>% 
  mutate(decay = if_else(variable ==  'covid','.5',decay))

model_table %>%
  datatable(rownames = NULL,
            options = list(scrollX = T,
                           dom = "t"))

The model table can be used as an input in the linea::run_model() function. The linea::response_curves() function will display the non-linear relationship capture by the model.

model = run_model(data = data,
                  dv = 'ecommerce',
                  model_table = model_table)

model %>% 
  response_curves(
    x_min = 0,
    x_max = 30,
    y_min = 0,
    y_max = 20000,
    interval = 0.01
    )

Testing

Unused Variables

The function linea::what_next() can be used to run a model for each of the unused variables in the data. The function will run the current model specification with the additional unused variable. This will return a data.frame containing the statistics of each model run.

model %>% 
  what_next() %>% 
  datatable(rownames = NULL)

## Warning: model object does not contain 'meta_data'. 
## Warning: model object does not contain 'id_var'.

Parameter Tuning

Similarly, to find the right parameters for the non-linear relationship, the function linea::what_trans() can be used to run multiple models with a range of parameters. If parameters are passed for multiple transformations, the function will run models for all combinations. The inputs for this function are:

the starting model
a variable name, present in the data
a table (i.e. trans_df) specifying the values of the parameters

The trans_df can be built as follows:

trans_df = data.frame(
  name = c('diminish', 'decay', 'lag', 'ma'),
  func = c(
    'linea::diminish(x,a)',
    'linea::decay(x,a)',
    'linea::lag(x,a)',
    'linea::ma(x,a)'
  ),
  order = 1:4
) %>%
  dplyr::mutate(val = '') %>%
  dplyr::mutate(val = dplyr::if_else(condition = name == 'diminish',
                                     '0.5,1,10,100,1000',
                                     val))


trans_df %>% 
  datatable(rownames = NULL)

model %>% 
  what_trans(trans_df = trans_df,
             variable ='offline_media') %>% 
  datatable(rownames = NULL)

GTrends

Google Trends can be a very useful source of data as google search volumes are often correlated with events and can be used as a proxy for a missing variable. The function linea::gt_f() will return the original dataframe with the added google trends variable.

# data = data %>%
#   gt_f(kw = 'ramadan',append = T) %>%
#   gt_f(kw = 'trump',append = T) %>%
#   gt_f(kw = 'covid',append = T) 
# 
# data %>%
#   datatable(options = list(scrollX = T),rownames = NULL)

Pools

Coming soon

LINEA

Getting Started

Prerequisites

Installation

Data Ingestion

First Model

Visualisation

Decomposition

Prediction, Actual, Error

Diagnostic Charts

Response Curves

Additional Features

Seasonality

Transformations

Decay

Diminish

Lag

Moving Average

Non-linear models

Testing

Unused Variables

Parameter Tuning

GTrends

Categories

Pools